Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index.

IF 2.5 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL
Reza Rezaiy, Ani Shabri
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Abstract

This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.

利用基于标准化降水指数的 EEMD-ARIMA 模型提高干旱预测精度。
本研究将集合经验模式分解(EEMD)与自回归综合移动平均(ARIMA)模型相结合,用于干旱预测。在干旱预测领域,我们利用阿富汗赫拉特省 1970 年 1 月至 2019 年 12 月的月降水量数据,对 EEMD-ARIMA 模型与传统 ARIMA 方法进行了评估。我们的评估跨越了标准化降水指数(SPI)3、SPI 6、SPI 9 和 SPI 12 的不同时间尺度。采用了均方根误差、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和 R2 等统计指标。为了全面了解数据特征,每个 SPI 序列最初都是根据原始月降水时间序列计算得出的。随后,使用 EEMD 对每个 SPI 进行分解,得到本征模式函数(IMF)和一个残差序列。下一步是使用相应的 ARIMA 模型对每个 IMF 部分和残差进行预测。为了对初始 SPI 序列进行集合预测,最后将建模的 IMF 和残差序列的预测结果相加。结果表明,与传统的 ARIMA 模型相比,EEMD-ARIMA 能显著提高干旱预测的准确性。
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来源期刊
Water Science and Technology
Water Science and Technology 环境科学-工程:环境
CiteScore
4.90
自引率
3.70%
发文量
366
审稿时长
4.4 months
期刊介绍: Water Science and Technology publishes peer-reviewed papers on all aspects of the science and technology of water and wastewater. Papers are selected by a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, development and application of new techniques, and related managerial and policy issues. Scientists, engineers, consultants, managers and policy-makers will find this journal essential as a permanent record of progress of research activities and their practical applications.
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